Most recent publications
Controversies in Artificial Intelligence in Neurosurgery
Recai Yilmaz, Samuel Browd, Daniel A Donoho
Publication date: 2025/1/1
Journal: Neurosurgery Clinics
Surgical practice has transcended its origins in rudimentary techniques, rituals, and shared beliefs, evolving into a domain driven by scientific rigor and technological advancement. Although human intellect has guided this progression to date, we now face the rise of advanced computational systems that can supplement our intelligence. Recent strides in data availability, computational prowess, and scientific innovation herald a new epoch of artificial intelligence (AI) systems with far-reaching effects across society. These AI systems, adept at performing large-scale data analyses through methods often inscrutable to human comprehension, present unprecedented opportunities for scientific and practical medical advancements. 1 Can AI systems and surgeons collaborate to enhance the safety and efficiency of neurosurgical procedures?
Across medicine, AI is often touted as offering unprecedented opportunities for …
A Microdiscectomy Surgical Video Annotation Framework for Supervised Machine Learning Applications
Kochai Jan Jawed, Ian Buchanan, Kevin Cleary, Elizabeth Fischer, Aaron Mun, Nishanth Gowda, Arhum Naeem, Recai Yilmaz, Daniel A Donoho
Publication date: 2024/10
Journal: International journal of computer assisted radiology and surgery
Purpose
Lumbar discectomy is among the most common spine procedures in the US, with 300,000 procedures performed each year. Like other surgical procedures, this procedure is not excluded from potential complications. This paper presents a video annotation methodology for microdiscectomy including the development of a surgical workflow. In future work, this methodology could be combined with computer vision and machine learning models to predict potential adverse events. These systems would monitor the intraoperative activities and possibly anticipate the outcomes.
Methods
A necessary step in supervised machine learning methods is video annotation, which involves labeling objects frame-by-frame to make them recognizable for machine learning applications. Microdiscectomy video recordings of spine surgeries were collected from a multi-center research collaborative. These videos were …
MR-guided focused ultrasound in pediatric neurosurgery: current insights, technical challenges, and lessons learned from 45 treatments at Children’s National Hospital
Gregory F Keating, Kelsi M Chesney, Nirali Patel, Lindsay Kilburn, Adriana Fonseca, Roger J Packer, Chaitanya Challa, Patrick F O’Brien, Daniel A Donoho, John S Myseros, Chima Oluigbo, Robert F Keating, Hasan R Syed
Publication date: 2024/9/1
Journal: Neurosurgical focus
OBJECTIVE
MR-guided focused ultrasound (MRgFUS) is an evolving technology with numerous present and potential applications in pediatric neurosurgery. The aim of this study was to describe the use of MRgFUS, technical challenges, complications, and lessons learned at a single children’s hospital.
METHODS
A retrospective analysis was performed of a prospectively collected database of all pediatric patients undergoing investigational use of MRgFUS for treatment of various neurosurgical pathologies at Children’s National Hospital. Treatment details, clinical workflow, and standard operating procedures are described. Patient demographics, procedure duration, and complications were obtained through a chart review of anesthesia and operative reports.
RESULTS
In total, 45 MRgFUS procedures were performed on 14 patients for treatment of …
The role of focused ultrasound for pediatric brain tumors: current insights and future implications on treatment strategies
Kelsi M Chesney, Gregory F Keating, Nirali Patel, Lindsay Kilburn, Adriana Fonseca, Cheng-Chia Wu, Javad Nazarian, Roger J Packer, Daniel A Donoho, Chima Oluigbo, John S Myseros, Robert F Keating,
Hasan R Syed
Publication date: 2024/8
Journal: Child's Nervous System
Introduction
Focused ultrasound (FUS) is an innovative and emerging technology for the treatment of adult and pediatric brain tumors and illustrates the intersection of various specialized fields, including neurosurgery, neuro-oncology, radiation oncology, and biomedical engineering.
Objective
The authors provide a comprehensive overview of the application and implications of FUS in treating pediatric brain tumors, with a special focus on pediatric low-grade gliomas (pLGGs) and the evolving landscape of this technology and its clinical utility.
Methods
The fundamental principles of FUS include its ability to induce thermal ablation or enhance drug delivery through transient blood-brain barrier (BBB) disruption, emphasizing the adaptability of high-intensity focused ultrasound (HIFU) and low-intensity focused ultrasound (LIFU) applications.
Results
Several ongoing clinical trials explore the potential of FUS in offering …
Automated Intraoperative Visual Detection of Pediatric Epileptogenic Brain Lesions Using a Machine Learning Classifier
Naomi Kifle, Bo Ning, In-Seok Song, Ava Jiao, Saige Teti, Daniel A Donoho, Jeremy Kang, Ashley Yoo, Chima Oluigbo, Robert Keating, Richard Jaepyeong Cha
Publication date: 2024/7/15
Journal: 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
450,000 children with epilepsy in the United States suffer lifelong disability and are at risk of sudden death. Surgical treatment of epilepsy is limited by the ability to visually discriminate between normal and abnormal brain tissue using visual light surgical microscopes: resection of excessive tissue can lead to neurologic injury, while insufficient resection often does not lead to durable cures. We propose a machine-learning-based segmentation model to identify epileptogenic, abnormal tissue thereby improving accuracy of surgical resection. We collected 62 frames from the live stream of an operating microscope during a pediatric epilepsy surgery. We trained a random forest classifier to segment full frame images into pathological tissue or background. We achieved an average specificity of 0.99, sensitivity of 0.34, and intersection over union of 0.28, despite the constraints of a limited dataset. Machine learning …
Anterior cervical discectomy and fusion for the treatment of pediatric Hirayama disease
Marc Prablek, Gabriel Reyes, Varun Kannan, Charles T Gay, Timothy E Lotze, Daniel A Donoho, David F Bauer
Publication date: 2024/5
Journal: Child's Nervous System
Purpose
Hirayama disease, a rare cervical myelopathy in children and young adults, leads to progressive upper limb weakness and muscle loss. Non-invasive external cervical orthosis has been shown to prevent further neurologic decline; however, this treatment modality has not been successful at restoring neurologic and motor function, especially in long standing cases with significant weakness. The pathophysiology remains not entirely understood, complicating standardized operative guidelines; however, some studies report favorable outcomes with internal fixation. We report a successful surgically treated case of pediatric Hirayama disease, supplemented by a systematic review and collation of reported cases in the literature.
Methods
A review of the literature was performed by searching PubMed, Embase, and Web of Science. Full-length articles were included if they reported clinical data regarding the …
Pediatric Endoscopic Endonasal Skull Base Reconstruction Surgical Techniques: A Systematic Review and Meta-analysis
Myra A Zaheer, Ari Ettleson, Abhisri Ramesh, Aadit Mehta, Arhum Naeem, Nish Gowda, Peter Harris, Daniel Donoho
Publication date: 2024/2
Journal: Journal of Neurological Surgery Part B: Skull Base
Methods: We searched MEDLINE and PubMed for articles published between January 1, 1990 and February 2, 2021. Two independent reviewers AM and MZ screened titles and abstracts for relevance, following PRISMA guidelines. Following full-text analysis, we arbitrated the 2 lists to include 24 studies. The PROSPERO and Cochrane databases were searched to ensure that no overlapping systematic reviews had been previously published. Individualized data from included studies was manually extracted and compiled for further analysis.
Results: Of the 250 patients identified, 97 (38.8%) had fat grafts, 63 (25.5%) had dural replacement, 38 (15.2%) had bone flaps, 81 (32.4%) had nasoseptal flaps, and 37 (14.8%) had fascia grafts. The use of a nasoseptal flap was correlated with an increased incidence of complications (14.81%) compared to all other previously mentioned skull base reconstruction techniques …
AI-Based Surgical Tools Detection from Endoscopic Endonasal Pituitary Videos
Margaux Masson-Forsythe, Juan Vivanco Suarez, Muhammad Ammar Haider, James K Liu, Daniel A Donoho
Publication date: 2024/2
Journal: Journal of Neurological Surgery Part B: Skull Base
Methods: Twenty-seven videos of EESs were manually labeled by a team of annotators (supervised by neurosurgical scientists) identifying 12 surgical tools: Doppler, drill, freer elevator, grasper, irrigation, Rhoton curette, Rhoton dissector, rongeur, scissor, suction, surgical knife, and unknown. The resulting dataset was split into an 80% training set and a 20% validation set. The annotated videos (labels) were used to develop a YoloV8 AI model, state-of-the-art computer vision model for object detection. The model is able to identify and localize surgical tools in each video frame. Performance accuracy was assessed using the manual labels in the validation set as ground truth ([Fig. 1]).
The Use of Virtual Reality for Surgical Guidance in a Two-Staged Combined Petrosal Approach for Resection of a Petroclival Meningioma: A Case Report
Hayes Patrick, Peter Harris, Myra Zaheer, Ashkan Monfared, Daniel Donoho
Publication date: 2024/2
Journal: Journal of Neurological Surgery Part B: Skull Base
The combined middle fossa and retrolabyrinthine approach was used for the resection of a large petroclival meningioma with brainstem compression. This complex operation was divided into two stages: extradural bone removal in the first stage, followed by division of the tentorium and intradural resection of the tumor in the second stage. A virtual reality model was rendered following the first stage to ensure optimum bony exposure and to provide guidance for the subsequent tumor resection.
The Relationship Between Procedural Volume, Hospital Quality, and Postoperative Mortality in Pediatric Neurosurgery: Review of the Literature
Carlos Aguilera, Kazi A Kalam, Kelsi Chesney, Daniel Donoho
Publication date: 2024/2/1
Journal: World Neurosurgery
Background
Studies of neurosurgical pediatric patients associate treatment at low-volume hospitals and by low-volume surgeons with increased odds of adverse outcomes. Although these associations suggest that increased centralization of care could be considered, we evaluate whether confounding endogenous factors mitigate against the proposed outcome benefits.
Methods
Literature review of English language articles from 1999 to 2021. We included articles that assessed volume-outcome effects in pediatric neurosurgical patients.
Results
Twelve papers were included from 1999 to 2021. Primary outcomes included mortality (9), length of stay (LOS) (6), complications (4), and shunt revision/failure rates (3). Volume was measured at the hospital level (8) and at the surgeon level (6). Four papers found that higher volume hospitals had lower odds of mortality. Two papers found that hospitals with higher volume had …
A vision transformer for decoding surgeon activity from surgical videos
Dani Kiyasseh, Runzhuo Ma, Taseen F Haque, Brian J Miles, Christian Wagner, Daniel A Donoho, Animashree Anandkumar, Andrew J Hung
Publication date: 2023/6
Journal: Nature Biomedical Engineering
The intraoperative activity of a surgeon has substantial impact on postoperative outcomes. However, for most surgical procedures, the details of intraoperative surgical actions, which can vary widely, are not well understood. Here we report a machine learning system leveraging a vision transformer and supervised contrastive learning for the decoding of elements of intraoperative surgical activity from videos commonly collected during robotic surgeries. The system accurately identified surgical steps, actions performed by the surgeon, the quality of these actions and the relative contribution of individual video frames to the decoding of the actions. Through extensive testing on data from three different hospitals located in two different continents, we show that the system generalizes across videos, surgeons, hospitals and surgical procedures, and that it can provide information on surgical gestures and skills from …